I. Introduction: Transcending Nationalism: The European Community 1958-2000

Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image.

A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm

Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.

Detection of skin disease using metaheuristic supported artificial neural networks

The current work proposes a neural based detection method of two different skin diseases using skin imaging. Skin images of two diseases namely Basel Cell Carcinoma and Skin Angioma are utilized. SIFT feature extractor has been employed followed by a clustering phase on feature space in order to reduce the number of features suitable for neural based models. The extracted bag-of-features modified dataset is used to train metaheuristic supported hybrid Artificial Neural Networks to classify the skin images in order to detect the diseases under study. A well-known multi objective optimization technique called Non-dominated Sorting Genetic Algorithm — II is used to train the ANN (NN-NSGA-II). The proposed model is further compared with two other well-known metaheuristic based classifier namely NN-PSO (ANN trained with PSO) and NN-CS (ANN trained with Cuckoo Search) in terms of testing phase confusion matrix based performance measuring metrics such as accuracy, precision, recall and F-measure. Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model.

Image based skin disease detection using hybrid neural network coupled bag-of-features

In medical systems for patient's authentication, keeping biometric data secure is a general problem. Many studies have presented various ways of protecting biometric data especially finger vein biometric data. Thus, It is needs to find better ways of securing this data by applying the three principles of information security aforementioned, and creating a robust verification system with high levels of reliability, privacy and security. Moreover, it is very difficult to replace biometric information and any leakage of biometrics information leads to earnest risks for example replay attacks using the robbed biometric data. In this paper presented criticism and analysis to all attempts as revealed in the literature review and discussion the proposes a novel verification secure framework based confidentiality, integrity and availability (CIA) standard in triplex blockchain-particle swarm optimization (PSO)-advanced encryption standard (AES) techniques for medical systems patient's authentication. Three stages are performed on discussion. Firstly, proposes a new hybrid model pattern in order to increase the randomization based on radio frequency identification (RFID) and finger vein biometrics. To achieve this, proposed a new merge algorithm to combine the RFID features and finger vein features in one hybrid and random pattern. Secondly, how the propose verification secure framework are followed the CIA standard for telemedicine authentication by combination of AES encryption technique, blockchain and PSO in steganography technique based on proposed pattern model. Finally, discussed the validation and evaluation of the proposed verification secure framework.

Based Medical Systems for Patient’s Authentication: Towards a New Verification Secure Framework Using CIA Standard

The Job Shop Scheduling Problem (known as JSSP) is a wellknown and one of the difficult combinatorial optimization problems and treated as a member of NP-complete problem class. This paper presents an algorithm based on Simulated Annealing method to solve the Job Shop Scheduling problem. It is an approximation algorithm for finding the minimum makespan in a job shop. The proposed algorithm is based on Roulette wheel selection and simulated annealing, a generalization of the well known and effective iterative improvement approach for combinatorial optimization problems. The generalization involves the acceptance of cost-increasing transitions with a nonzero probability to avoid getting stuck in local minima. The problem studied in this research focuses on the sequencing of operations and allocation of operation to the machine under some sequence constraint.

Blending roulette wheel selection with simulated annealing for job shop scheduling problem

The Job-Shop Scheduling Problem (JSSP) is a well-known and one of the challenging combinatorial optimization problems and falls in the NP-complete problem class. This paper presents an algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem. The procedure is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop. The proposed algorithm is based on Genetic algorithm and simulated annealing. SA is an iterative well known improvement to combinatorial optimization problems. The procedure considers the acceptance of cost-increasing solutions with a nonzero probability to overcome the local minima. The problem studied in this research paper moves around the allocation of different operation to the machine and sequencing of those operations under some specific sequence constraint.

http://www.sersc.org/journals/IJHIT/vol8_no11_2015/23.pdf

An Efficient Approach to Job Shop Scheduling Problem using Simulated Annealing

Evidently, information security is a sine qua non in the “modern life, With its sick hurry, its divided aims, Its heads o'ertaxed, its palsied hearts, …”. To protect our data against unauthorized access, from the time immemorial the first choice has always been to use cryptography. With rapid growth in the arena of multimedia technology, since digital image has become an important medium of communication, extensive research is still a dynamic process in this field. We also have concentrated on image data encryption in this work. The effectiveness of the protection through encryption depends on the algorithm applied and as well as on the quality of the ‘key’ used. If a ‘key’ is badly designed or haphazardly selected, obviously the protection fails to provide proper security and improper access can be gained on the secured information. The first algorithm in cryptographic system design is the algorithm to generate ‘key’. It specifies the manner in which the ‘key’ is to be chosen. This work focuses on a totally new approach towards the ‘key’ generation for encryption algorithms. Here, Genetic Algorithm (GA), an important method of artificial intelligence has been applied to generate encryption ‘key’, which plays a vital role in any type of encryption. In our work, a hybridized technique called BlowGA is also proposed which is a combination of Blowfish and GA. Blowfish Algorithm is a conventional method of encryption. Our experimental observations show that the newly-proposed hybridized method BlowGA outperforms both GA and Blowfish Algorithm.

Image cryptography: The genetic algorithm approach

This paper presents a new approach of steganography. The secret image is firstly perturbed by Stego-Key (SK) and again perturbed by a genetically generated, good, session based transposition operator (TO). In the next step this perturbed secret image is embedded within the host image by variable bit replacement using a hash function. At the time of extraction of secret image firstly perturbed image is extracted then by reverse engineering with the session based TO and SK the secret image is obtained.

A Steganographic Approach by Using Session Based Stego-Key, Genetic Algorithm and Variable Bit Replacement Technique

With the increasing use of digital techniques for transmitting and storing images, the fundamental issue of protecting image data against unauthorized access, chaos-based encryption has enjoined a superb know-how to deal with the problem of fast and highly-secured image encryption. The effectiveness of the transposition cipher algorithm profusely depends on the trait of the key. If a key is badly designed or poorly selected, then the protection fails to provide proper security and improper access can be gained on the sensitive information. This work focuses on the application of a crucial Artificial Intelligence approach in block-based image transposition cipher generation. Here, Genetic Algorithm has been applied to search for effective keys to encrypt images through permutation of the pixel locations. This work also statistically analyses the effect of applying varied sizes of keys in block- based image transposition algorithm.

Sandeep Bhowmik

Papers

Blending roulette wheel selection with simulated annealing for job shop scheduling problem

An Efficient Approach to Job Shop Scheduling Problem using Simulated Annealing

Image cryptography: The genetic algorithm approach

A Steganographic Approach by Using Session Based Stego-Key, Genetic Algorithm and Variable Bit Replacement Technique

Application of GA in Key Generation for Image Transposition Cipher Algorithm